Tree survey and allometric models for tiger bush in northern Senegal and comparison with tree parameters derived from high resolution satellite data

Rasmussen, Mads Olander; Göttsche, Frank-M.; Diop, Doudou; Mbow, Cheikh; Olesen, F.; Fensholt, Rasmus; Sandholt, Inge

doi:10.1016/j.jag.2011.01.007

Cited by 38 publications

(32 citation statements)

References 26 publications

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“…The results were verified in a quality assessment and agree well with the Tree Crown Cover (TCC) fraction determined in a previous survey in the validation area [55], where a TCC between 3% and 4.5% was found by manually classifying five sample regions. Due to the small TCC value, the effect of varying TCC between 3% and 6% on retrieved LST is negligible [18].…”

Section: Estimation Of Land Surface Cover and Representative In-situ supporting

confidence: 85%

Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

et al. 2016

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Abstract:Since 2005, the Land Surface Analysis Satellite Application Facility (LSA SAF) operationally retrieves Land Surface Temperature (LST) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG). The high temporal resolution of the Meteosat satellites and their long term availability since 1977 make their data highly valuable for climate studies. In order to ensure that the LSA SAF LST product continuously meets its target accuracy of 2˝C, it is validated with in-situ measurements from four dedicated LST validation stations. Three stations are located in highly homogenous areas in Africa (semiarid bush, desert, and Kalahari semi-desert) and typically provide thousands of monthly match-ups with LSA SAF LST, which are used to perform seasonally resolved validations. An uncertainty analysis performed for desert station Gobabeb yielded an estimate of total in-situ LST uncertainty of 0.8˘0.12˝C. Ignoring rainy seasons, the results for the period 2009-2014 show that LSA SAF LST consistently meets its target accuracy: the highest mean root-mean-square error (RMSE) for LSA SAF LST over the African stations was 1.6˝C while mean absolute bias was 0.1˝C. Nighttime and daytime biases were up to 0.7˝C but had opposite signs: when evaluated together, these partially compensated each other.

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Section: Estimation Of Land Surface Cover and Representative In-situ supporting

confidence: 85%

Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

et al. 2016

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“…Herrmann, Wickhorst, & Marsh, 2013;Karlson, Reese, & Ostwald, 2014;Rasmussen et al, 2011;Sterling & Orr, 2014;San Emeterio & Mering, 2012). However, imageries with a spatial resolution of 1-5 m are cumbersome to process, expensive, susceptible to clouds, and do only provide a static situation for a limited spatial area.…”

Section: Introductionmentioning

confidence: 99%

Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

Brandt

Hiernaux

Tagesson

et al. 2016

Remote Sensing of Environment

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From in situ measured woody cover we develop a phenology driven model to estimate the canopy cover of woody species in the Sahelian drylands at 1 km scale. The model estimates the total canopy cover of all woody phanerophytes and the concept is based on the significant difference in phenophases of dryland trees, shrubs and bushes as compared to that of the herbaceous plants. Whereas annual herbaceous plants are only green during the rainy season and senescence occurs shortly after flowering towards the last rains, most woody plants remain photosynthetically active over large parts of the year. We use Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l'Observation de la Terre (SPOT) -VEGETATION (VGT) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series and test 10 metrics representing the annual FAPAR dynamics for their ability to reproduce in situ woody cover at 43 sites (163 observations between 1993 and 2013) in the Sahel. Both multi-year field data and satellite metrics are averaged to produce a steady map. Multiple regression models using the integral of FAPAR from the onset of the dry season to the onset of the rainy season, the start date of the growing season and the rate of decrease of the FAPAR curve achieve a cross validated r 2 /RMSE (in % woody cover) of 0.73/3.0 (MODIS) and 0.70/3.2 (VGT). The extrapolation to Sahel scale shows agreement between VGT and MODIS at an almost nine times higher woody cover than in the global tree cover product MOD44B which only captures trees of a certain minimum size. The derived woody cover map of the Sahel is made publicly available and represents an improvement of existing products and a contribution for future studies of drylands quantifying carbon stocks, climate change assessment, as well as parametrization of vegetation dynamic models.

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“…These results are similar or better when compared to related research where high resolution satellite data and GEOBIA has been used for mapping TCC (Rasmussen et al 2011, Morales, Miura, andIdol 2008). For example, Rasmussen et al (2011) reported a moderate agreement (R 2 = 0.51) between field data and TCC derived from Quickbird data for an area in northern Senegal. Morales et al (2008) reported R 2 = 0.86 and MAE = 2% when using Ikonos data in a Hawaiian dry forest.…”

Section: Level Of Aggregationmentioning

confidence: 95%

Remote Sensing of Woodland Structure and Composition in the Sudano-Sahelian zone : Application of WorldView-2 and Landsat 8

Karlson¹

2015

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Woodlands constitute the subsistence base of the majority of people in the Sudano-Sahelian zone (SSZ). Trees and grasses provide key ecosystem goods and services, including soil protection, fuelwood, food products and fodder. However, climate change in combination with rapidly increasing populations and altered land use practices put increasing pressure on the vegetation cover in this region. Low availability of in situ data on vegetation structure and composition hampers research and monitoring of this essential resource. Satellite and aerial remote sensing represents important alternative data sources in this context. The main advantages of remote sensing are that information can be collected with high frequency over large geographical areas at relatively low costs. This thesis explores the utility of remote sensing for mapping and analysing vegetation, primarily trees, in the SSZ. A comprehensive literature review was first conducted to describe how the application of remote sensing for analysing vegetation has developed in the SSZ between 1975 and 2014, and to identify important research gaps. Based on the gaps identified in the literature review, the capabilities of two new satellite sensors (WorldView-2 and Landsat 8) for mapping woodland structure and composition were tested in an agroforestry landscape located in central Burkina Faso. The tree attributes in focus included tree crown area (m 2 ), tree species, tree canopy cover (%) and aboveground biomass (tons ha -1 ). The data processing methods encompassed objectbased image analysis for tree crown delineation, and use of the Random Forest algorithm for tree species classification (WorldView-2) and estimation of tree canopy cover and aboveground biomass (Landsat 8).The literature review revealed that the use of remote sensing has increased extensively in the SSZ, especially since 2010. Remote sensing is increasingly used by diverse scientific disciplines although the contribution from African authors remains relatively low. The main application area has been to analyze changes in vegetation productivity and broad vegetation types, while relatively few studies have used remote sensing to map tree attributes at a higher level of detail, and to analyze interactions between the vegetation cover and environmental factors.This thesis shows that the WorldView-2 satellite represents a useful data source for mapping individual tree attributes, including tree crown area and tree species. The individual tree crown delineation achieved promising results: 85.4% of the reference trees were detected in the WorldView-2 data and tree crown area was estimated with an average error of 45.6%. Both detection and delineation accuracy was influenced by tree size, the degree of crown closure and the composition of the undergrowth vegetation. In addition, WorldView-2 data produced high classification accuracies for five locally important tree species, which are common throughout the SSZ. The highest overall classification accuracy (82.4%) was produced using multi-tempor...

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Tree survey and allometric models for tiger bush in northern Senegal and comparison with tree parameters derived from high resolution satellite data

Cited by 38 publications

References 26 publications

Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

Remote Sensing of Woodland Structure and Composition in the Sudano-Sahelian zone : Application of WorldView-2 and Landsat 8

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